Protease-mediated drug delivery
Bibliographic record
Abstract
Drugs used in disease treatment can cause damage to both malignant and normal tissue. This toxicity limits the maximum therapeutic dose. Drug targeting is of high interest to increase the therapeutic efficacy of the drug without increasing systemic toxicity. Certain tissue abnormalities, disease processes, cancers, and infections are characterized by high levels of activity of specific extracellular and/or intracellular proteases. Abnormally high activity levels of specific proteases are present at sites of physical or chemical trauma, blood clots, malignant tumors, rheumatoid arthritis, inflammatory bowel disease, gingival disease, glomerulonerphritis, and acute pancreatitis. Abnormal protease activity is suspected in development of liver thrombosis, pulmonary emphysema, atherosclerosis, and muscular dystrophy. Inactiviating disease-associated proteases by the administration of appropriate protease inhibitors has had limited success. Instead, one could use such proteases to target drugs to treat the condition. Protease mediated drug delivery offers such a possibility. Solubilizing groups are attached to insoluble drugs via a polypeptide chain which is specifically cleavable by certian proteases. When the solubilized drug enounters the protease, the solubilizing moieties are cleaved, and the drug precipitates at the disease location. Thus, a smaller systemic dosage could result in a therapeutic drug concentration at the treatment site with less systemic toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".